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Abstract #0711

Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning

Antoine Legouhy1, Ross Callaghan2, Whitney Stee3,4, Philippe Peigneux3,4, Hojjat Azadbakht2, and Hui Zhang1
1Hawkes Institute & Department of Computer Science, University College London, London, United Kingdom, 2AINOSTICS ltd., Manchester, United Kingdom, 3UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN – Centre for Research in Cognition and Neurosciences and UNI, Université Libre de Bruxelles, Bruxelles, Belgium, 4GIGA - Cyclotron Research Centre - In Vivo Imaging, ULiège, Liège, Belgium

Synopsis

Keywords: Diffusion Modeling, Data Processing, Distortion correction

Motivation: Eddy-current-induced distortions in diffusion MRI (dMRI) cause misalignment between volumes, disrupting downstream modeling and analysis. Current correction methods rely on traditional optimization, which is computationally intensive.

Goal(s): To develop a deep-learning approach that efficiently corrects for eddy-current distortions in dMRI, offering a faster alternative to slow traditional methods.

Approach: Two deep-learning models in sequence, both convolutional: 1) An image translator to restore correspondences between volumes. 2) A registration model to estimate the distortion parameters and apply correction.

Results: The proposed method achieves distortion correction comparable to the widely-used FSL Eddy tool, but very rapidly at inference.

Impact: Together with recently developed deep-learning susceptibility-induced distortion correction techniques, this work paves the way for real-time preprocessing of dMRI, facilitating its wider uptake in the clinic.

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